Implementation

Historically, ancillary services prices have shown a strong correlation with energy prices. To quantify this relationship, a regression model was developed using ERCOT’s historical day-ahead hub average prices and ancillary services prices.

This model is then used to forecast ancillary services prices based on the energy price outputs from the production-cost model. Additionally, a decay factor is incorporated to introduce a long-term decline in daily price patterns generated by the regression, accounting for the saturation effect caused by an increasing supply of batteries, which exerts downward pressure on prices. The correlation between ancillary services prices and energy price outputs is illustrated in the graph above.